[1]彭荣荣),刘芸男),杨冬燕),等.临床用血需求的ARIMA模型预测[J].郑州大学学报(医学版),2019,(06):874-878.[doi:10.13705/j.issn.1671-6825.2018.12.166]
 PENG Rongrong),LIU Yunnan),YANG Dongyan),et al.Prediction of clinical blood demand based on the ARIMA model[J].JOURNAL OF ZHENGZHOU UNIVERSITY(MEDICAL SCIENCES),2019,(06):874-878.[doi:10.13705/j.issn.1671-6825.2018.12.166]
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临床用血需求的ARIMA模型预测()
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《郑州大学学报(医学版)》[ISSN:1671-6825/CN:41-1340/R]

卷:
期数:
2019年06期
页码:
874-878
栏目:
应用研究
出版日期:
2019-11-20

文章信息/Info

Title:
Prediction of clinical blood demand based on the ARIMA model
作者:
彭荣荣1)刘芸男1)杨冬燕2)王含柔1)赵明烽1)杨小丽1)
1)重庆医科大学公共卫生与管理学院; 医学与社会发展研究中心; 健康领域社会风险预测治理协同创新中心 重庆 400016 2)重庆市血液中心 重庆 400015
Author(s):
PENG Rongrong1) LIU Yunnan1) YANG Dongyan2) WANG Hanrou1) ZHAO Mingfeng1) YANG Xiaoli1)
1)School of Public Health and Management,Chongqing Medical University; Research Center for Medical and Social Development; Innovation Center for Social Risk Governance in Health,Chongqing 400016 2)Chongqing Blood Center,Chongqing 400015
关键词:
ARIMA模型 悬浮红细胞 临床需求预测
Keywords:
autoregressive integrated moving average model suspended red blood cell clinical demand prediction
分类号:
R457.1; R193.3
DOI:
10.13705/j.issn.1671-6825.2018.12.166
摘要:
目的:探讨ARIMA模型预测临床用血量的可行性,为科学预测临床用血量及制定采血招募计划提供数据支持。方法:利用SPSS对重庆市万州中心血站2006年1月~2016年6月每月悬浮红细胞临床用量建立ARIMA模型,经模型识别、参数估计与检验,确定最优模型; 运用该模型预测2016年7~12月每月悬浮红细胞临床用量,并与实际值比较,验证预测效果。结果:最优模型为ARIMA(3,1,0)(0,1,1)12,残差序列的ACF和PACF落在95%CI内,Ljung-Box统计量差异无统计学意义(P>0.05),说明残差序列呈白噪声,模型通过检验。该模型的预测值均在95%CI内,并且预测值与实际值比较,动态趋势基本一致,平均相对误差为4.27%,预测精度较高。结论:ARIMA(3,1,0)(0,1,1)12模型能较好地拟合临床用血量在时间序列上的变化趋势,可用于临床用血量的预测。
Abstract:
Aim:To explore the feasibility of autoregressive integrated moving average(ARIMA)model in predicting clinical demand of blood, and to provide data support for scientifically predicting the clinical demand of blood and making the plan of blood collection and recruitment.Methods:SPSS was used to establish the ARIMA model based on the monthly clinical usage of suspended red blood cells from January 2006 to June 2016 in Wanzhou Central Blood Station, Chongqing. The optimal model was determined by model identification, parameter estimation and white noise test,and used to predict the monthly clinical demand of suspended red blood cells from July to December 2016.Results:The optimal model was ARIMA(3,1,0)(0,1,1)12, the residual sequence autocorrelation function and the partial autocorrelation function fell within the 95% confidence interval, and the Ljung-Box statistical results showed no significant difference(P>0.05), indicating that the residual sequence showed white noise and the model passed the test. The predicted values of the model were in the 95% confidence interval.Compared with the actual values of the clinical usage of suspended red blood cells in the same period, the dynamic trend was basically the same, which indicated prediction accuracy was high with a mean relative error of 4.27%.Conclusion:The ARIMA(3,1,0)(0,1,1)12 model can better fit the trend of clinical usage of suspended red blood cells in time series, and is feasible to apply the model of ARIMA to predict the blood for clinical usage.

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备注/Memo

备注/Memo:
【基金项目】重庆市决策咨询与管理创新计划项目(cstc2016jccxBX0064) 【作者简介】杨小丽,通信作者,女,1963年3月生,博士,教授,研究方向:卫生事业管理和社会保障,E-mail:872463319@qq.com
更新日期/Last Update: 2019-11-20